基于多视角视频的运动重建

为了更逼真地从视频图像序列中实现三维人体骨架动画形式的提取,以便进一步地对人体运动进行分析与研究,提出了一种基于多视角视频的运动重建的方法。该方法充分利用了标记点的信息,其核心步骤有标定摄像机,提取标记点,跟踪标记点和人体运动三维重建四个主要方面。其中,在跟踪标记点时,使用了基于多视觉的目标跟踪算法,该算法由结合了扩展卡尔曼滤波预测与标记点轨迹平滑性约束所构成的双目立体视觉跟踪与多目视觉数据融合两个方面。实验结果证明了所提方法的有效性与可行性。     

基于视频的运动捕获

现有的运动捕获方法大都存在运动捕获设备昂贵、演员运动受限等缺点，为此，提出了一种利用视觉技术从视频中提取人体运动的方法，并对其中的特片跟踪和三维运动序列恢复等关键技术进行了深入研究。基于人体模型的特征跟踪算法利用卡尔曼滤波和极线方程，能精确地跟踪比较大的人体运动；采用不共面的非线性定标模型和考虑运动不确定性的三维重建方法，能恢复逼真的三维人体骨架模型，实验结果验证了基于视频的运动捕获方法的可行性和有效性。     

基于sift特征匹配的人体上半身三维运动跟踪

研究人体姿态与视频优化跟踪问题,单目视频缺少深度信息,使得单目视频的人体运动跟踪难以实现三维姿态恢复问题。为解决上述问题,提出了一种利用sift特征尺度不变性的优点进行人体上半身三维运动跟踪的算法。在跟踪过程中先计算初始匹配sift特征点对,然后反复迭代出除误匹配点,消除误差,最后求解由两个匹配sift特征组成的方程组得到胸部关节的位姿,根据人体骨骼模型采用深度遍历依次恢复其它关节的姿态。实验结果表明,系统能够对人体上半身运动进行比较准确的三维运动跟踪。     

使用多视图L1跟踪器的三维人体运动恢复

近年来从视频中恢复三维人体运动的研究发展很快,其中大部分方法是基于前景轮廓的。提出了一种基于纹理信息的三维人体运动恢复方法,并给出了一个鲁棒、自适应的跟踪器模型。该模型基于L1跟踪器,并将其扩展到多个视图中,使用分层搜索来跟踪人体的各个部位。它可以寻找在模板子空间里重构误差最小的跟踪目标,将每个视图的重构误差作为衡量人体三维姿态与图像拟合的可能性函数。整个算法在退火粒子滤波的框架下进行。为了提高跟踪准度,在纹理模板更新过程中使用了两种方法:用人体的三维模型来检测自遮挡;根据模板系数检测计算错误的跟踪结果。综合这两种检测器,可以防止遮挡后和计算错误的跟踪结果加入到纹理模板中。在HumanEva-Ⅱ测试集上的实验表明,该算法能够得到较好的结果。     

一种强壮的三维运动盲水印算法

提出了一种基于DCT变换的三维运动盲水印算法，算法结合了扩展频谱和量化的思想，通过对DCT域中频系数进行适当量化扰动来嵌入水印信息。选择合理的量化步长能保证对原运动信号不产生明显的视觉破坏；同时通过扩展频谱，让每一位水印信息包含在多个频率系数中，能有效增加抗攻击能力。实验表明，该算法对高斯白噪声、重采样、运动光滑以及重排列等有较好的鲁棒性。     

结合模型匹配与特征跟踪的人体上半身三维运动姿态恢复方法

人体姿态空间的高维性及单目视频深度信息丢失,导致从单目视频恢复人体三维运动姿态非常困难,为此,利用特征跟踪的快速性及模型匹配的鲁棒性,提出一种无标记人体上半身三维运动跟踪方法.该方法利用匹配SIFT特征,并根据长度不变性约束建立优化目标函数,再采用迭代优化算法得到全局运动位姿;其他关节的姿态先根据逆运动学计算初始估计值,并通过模型匹配验证其可信度,当初始姿态估计错误时,则使用局部搜索获得关节姿态.实验结果表明,文中方法可以准确地恢复单目视频中人体上半身三维运动姿态.     

单目视频人体三维运动高效恢复

为解决计算机图形学和视觉领域的人体数据运动获取问题,提出一种从无标记点的单目视频恢复三维人体运动的方法.首先对人体侧影进行分析,获取躯干和未端节点位置信息;然后进行三维姿态优化.根据人体骨架特点,提出一个有效且计算简单的目标函数以及一种迭代优化策略,极大地减少了优化过程的计算量;设计了一个新颖的姿态序列恢复流程,克服了误差累积等传统跟踪方法的缺点.实验结果表明,文中方法可以准确地对视频中的复杂人体运动进行三维恢.     

双目视觉下三维人体运动跟踪算法*

由于人体运动的复杂性,人体运动轨迹的快速改变和人体自遮挡现象经常发生,这给人体运动跟踪带来了很大的困难。针对此问题提出了一种基于三维Kalman滤波器和人体约束的人体运动跟踪算法。该算法首先利用外极线约束和灰度互相关法对二维标记点进行立体匹配,计算各个标记点的三维位置,从而构建得到三维标记点;然后利用三维Kalman滤波器对三维标记点进行跟踪;最后利用人体约束检验和修正跟踪结果。实验结果表明,该算法能有效地对复杂人体动作进行跟踪并能从跟踪错误中正确恢复。     

基于二维关节型人体模型和EM算法的人体跟踪

提出一种跟踪单眼图像序列中的行人,并恢复其运动参数的新方法.在跟踪中采用了基于SPM(Sealed Prismat Model)扩展的二维纸板人模型取代三维人体模型,以获取更快的计算速度.作者使用EM算法在概率框架下进行运动估计,同时,算法也考虑了混合的运动模型和运动约束,以减小解的搜索空间.试验结果证明了该方法的有效性.     

基于视频序列的人体运动分析系统的研究与实现

基于视频的人体运动分析是通过视频序列对人体的运动进行检测、分割和跟踪，获得人体或者某一部位的运动信息，生成人体的运动轨迹以及各运动学和动力学参数，达到分析人体三维运动的目的。人体运动属于连接型非刚体运动的范畴，具有高度的非线性特点，利用视频进行人体运动分析，有着极大的研究价值和现实应用前景。本文以单目视频中人体的跳远运动为研究对象．研究与实现了一个基于视频序列的人体运动分析系统。     

3D Articulated Models and Multiview Tracking with Physical Forces

We present a method for automatically estimating the motion of an articulated object filmed by two or more fixed cameras. We focus our work on the case where the quality of the images is poor, and where only an approximation of a geometric model of the tracked object is available. Our technique uses physical forces applied to each rigid part of a kinematic 3D model of the object we are tracking. These forces guide the minimization of the differences between the pose of the 3D model and the pose of the real object in the video images. We use a fast recursive algorithm to solve the dynamical equations of motion of any 3D articulated model. We explain the key parts of our algorithms: how relevant information is extracted from the images, how the forces are created, and how the dynamical equations of motion are solved. A study of what kind of information should be extracted in the images and of when our algorithms fail is also presented. Finally we present some results about the tracking of a person. We also show the application of our method to the tracking of a hand in sequences of images, showing that the kind of information to extract from the images depends on their quality and of the configuration of the cameras.     

Tracking nonrigid motion and structure from 2D satellite cloudimages without correspondences

Tracking both structure and motion of nonrigid objects from monocular images is an important problem in vision. In this paper, a hierarchical method which integrates local analysis (that recovers small details) and global analysis (that appropriately limits possible nonrigid behaviors) is developed to recover dense depth values and nonrigid motion from a sequence of 2D satellite cloud images without any prior knowledge of point correspondences. This problem is challenging not only due to the absence of correspondence information but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In our method, the cloud images are segmented into several small regions and local analysis is performed for each region. A recursive algorithm is proposed to integrate local analysis with appropriate global fluid model constraints, based on which a structure and motion analysis system, SMAS, is developed. We believe that this is the first reported system in estimating dense structure and nonrigid motion under scaled orthographic views using fluid model constraints. Experiments on cloud image sequences captured by meteorological satellites (GOES-8 and GOES-9) have been performed using our system, along with their validation and analyses. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging and have many potential applications in earth and space sciences, especially in cloud models for weather prediction     

利用几何代数进行线段模型匹配和运动估计

首先探讨了Clifford代数（几何代数）在计算机视觉中的应用,并得到了2D与3D旋转的统一表达公式,进而探讨了该公式在直线模型匹配和运动估计中的应用;在改进2D多角弧匹配算法的基础上,提出了一个同时进行线段模型的匹配和运动估计的算法。该算法通过最小化模型线段与被检测线段间的距离（距离函数定义为对应点间欧氏距离的积分）而求得的最佳运动估计中的旋转,可由一个矩阵的奇异值分解来表示,从而为首次同时解决这两个问题,进行了初步尝,且该算法不受维数限制。最后的模拟实验结果表明,该算法效果良好。     

外极面图像的运动遮挡模型和运动纹理方向检测算法

运动遮挡边界处的运动估计是一种困难的问题,外极面图像方法将运动估计转化为转迹线的检测,人造物体的轨迹线容易通过边缘跟踪的方法获得,但对于纹理复杂的自然景物,轨迹跟踪较为困难。     

Snake terrestrial locomotion synthesis in 3D virtual environments

We present a method for a 3D snake model construction and terrestrial snake locomotion synthesis in 3D virtual environments using image sequences. The snake skeleton is extracted and partitioned into equal segments using a new iterative algorithm for solving the equipartition problem. This method is applied to 3D model construction and at the motion analysis stage. Concerning the snake motion, the snake orientation is controlled by a path planning method. An animation synthesis algorithm, based on a physical motion model and tracking data from image sequences, describes the snake’s velocity and skeleton shape transitions. Moreover, the proposed motion planning algorithm allows a large number of skeleton shapes, providing a general method for aperiodic motion sequences synthesis in any motion graph. Finally, the snake locomotion is adapted to the 3D local ground, while its behavior can be easily controlled by the model parameters yielding the appropriate realistic animations.     

A Variational Framework for Structure from Motion in Omnidirectional Image Sequences

We address the problem of depth and ego-motion estimation from omnidirectional images. We propose a correspondence-free structure-from-motion problem for sequences of images mapped on the 2-sphere. A novel graph-based variational framework is first proposed for depth estimation between pairs of images. The estimation is cast as a TV-L1 optimization problem that is solved by a fast graph-based algorithm. The ego-motion is then estimated directly from the depth information without explicit computation of the optical flow. Both problems are finally addressed together in an iterative algorithm that alternates between depth and ego-motion estimation for fast computation of 3D information from motion in image sequences. Experimental results demonstrate the effective performance of the proposed algorithm for 3D reconstruction from synthetic and natural omnidirectional images.     

Range Scan Registration Using Reduced Deformable Models

We present an unsupervised method for registering range scans of deforming, articulated shapes. The key idea is to model the motion of the underlying object using a reduced deformable model. We use a linear skinning model for its simplicity and represent the weight functions on a regular grid localized to the surface geometry. This decouples the deformation model from the surface representation and allows us to deal with the severe occlusion and missing data that is inherent in range scan data. We formulate the registration problem using an objective function that enforces close alignment of the 3D data and includes an intuitive notion of joints. This leads to an optimization problem that we solve using an efficient EM-type algorithm. With our algorithm we obtain smooth deformations that accurately register pairs of range scans with significant motion and occlusion. The main advantages of our approach are that it does not require user specified markers, a template, nor manual segmentation of the surface geometry into rigid parts.     

特种复杂试验装置中模型动态建模系统设计

为克服特种复杂试验装置中模型不可接触、传统动态建模方式周期长等困难,设计一种基于运动恢复结构算法的动态建模系统,实现实时的动态建模,以更直观和精确的指导试验。试验装置内设置车道线,使用智能寻迹车进行轨迹识别,环绕模型行进。智能寻迹车上同时搭载光学相机和红外相机,同视角拍摄特种复杂试验装置中模型的图像。使用运动恢复结构算法,从光学相机拍摄的图像重建模型的三维结构。模型上设置有环状编码标记点,利用标记点的坐标增加重建的精度。使用改进的运动恢复结构算法,从红外相机拍摄的图像重建红外三维结构,并建立起与光学图像生成的三维结构的对应关系。系统最终获得具有温度信息的可视化模型,并可使用头戴式虚拟现实设备进行沉浸式体验。     

视频中不完全运动特征的跟踪算法

在基于视频的人体运动捕获中，常用的特征跟踪算法对光照条件、图像噪音等非常敏感，而且不规则运动常使特征点或重叠、或自遮挡、或从视域中消失，给视频中运动特征跟踪带来很大的困难．为了有效地跟踪这些不完全运动的特征，提出一种有效的特征跟踪算法．实验结果表明，该方法不但能快速、精确地跟踪孤立特征，而且能有效地解决视频序列中存在较大噪音和不完全运动特征的跟踪问题．